{
  "cells": [
    {
      "metadata": {
        "id": "DhWmZgAVwDGu",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Copyright 2021 Google LLC\n",
        "\n",
        "Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "you may not use this file except in compliance with the License.\n",
        "You may obtain a copy of the License at\n",
        "\n",
        "    https://www.apache.org/licenses/LICENSE-2.0\n",
        "\n",
        "Unless required by applicable law or agreed to in writing, software\n",
        "distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "See the License for the specific language governing permissions and\n",
        "limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IMdCi5Ei9UE5"
      },
      "source": [
        "# Overview\n",
        "This notebook loads in all the simulated truncations (with model scores) as well as the experimentally tested subset of truncations. It then creates a plot showing the distribution of model scores for each tested truncation."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2MtlkKnbvzIv"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "priK_Jn-zFls"
      },
      "source": [
        "## Experimental PD affinity estimate \n",
        "\n",
        "These affinity estimates are fairly small so we directly embed them below."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "H851rxgDzRO_"
      },
      "outputs": [],
      "source": [
        "# Tuple of form: (Seq, Stringency, Kd)\n",
        "\n",
        "validated_core_seqs = [ ('TTTGGTGGATAGTAA', 1, '\u003c 512 nM'),\n",
        "                        ('AGAGGATTTGGTGGATAGT', 0, '\u003e 512nM'),\n",
        "                        ('AGAGGATTTGGTGGATAGTAAAT', 3, '\u003c 32 nM'),\n",
        "                        ('GAGGATTTGGTGGATAGTAAATC', 4, '\u003c 8 nM'),\n",
        "                        ('GAGGATTTGGTGGATAGTAAATCTTTG', 4, '\u003c 8 nM'),\n",
        "                        ('AAGAGGATTTGGTGGATAGTAAATCTT', 4, '\u003c 8 nM'),\n",
        "                        ('CAAGAGGATTTGGTGGATAGTAAATCTTTGC', 4, '\u003c 8 nM'),\n",
        "                        ('GATAGTAAATCTTTGCCTATCCA', 0, '\u003e 512nM'),\n",
        "                        ('GTGGATAGTAAATCTTTGCCTATCCAG', 0, '\u003e 512nM'),\n",
        "                        ('TTTGGTGGATAGTAAATCTTTGC', 0, '\u003e 512nM'),\n",
        "                        ('CAAGAGGATTTGGTGGATAGTAAATCTTTGCCTAT', 3, '\u003c 32 nM'),\n",
        "                        ('CAAGAGGATTTGGTGGATAGTAAATCTTTGCCTATCCAG', 3, '\u003c 32 nM'),\n",
        "                        ('GTTTTTGGTGGATAG', 0, '\u003e 512nM'),\n",
        "                        ('GTTTTTGGTGGATAGCAAA', 3, '\u003c 32 nM'),\n",
        "                        ('ACGTTTTTGGTGGATAGCAAATG', 3, '\u003c 32 nM'),\n",
        "                        ('ACGTTTTTGGTGGATAGCAAATGCCAG', 3, '\u003c 32 nM'),\n",
        "                        ('ACGTTTTTGGTGGATAGCAAATGCCAGGGCC', 3, '\u003c 32 nM'),\n",
        "                        ('ACGTTTTTGGTGGATAGCAAATGCCAGGGCCCTTT', 3, '\u003c 32 nM'),\n",
        "                        ('ACGTTTTTGGTGGATAGCAAATGCCAGGGCCCTTTTTTG', 3, '\u003c 32 nM'),\n",
        "                        ('GGACTGGTGGATAGT', 0, '\u003e 512nM'),\n",
        "                        ('CGGACTGGTGGATAGTAGA', 1, '\u003c 512 nM'),\n",
        "                        ('CGGACTGGTGGATAGTAGAGCTG', 0, '\u003e 512nM'),\n",
        "                        ('CACGGACTGGTGGATAGTAGAGC', 1, '\u003c 512 nM'),\n",
        "                        ('CACGGACTGGTGGATAGTAGAGCTGTG', 3, '\u003c 32 nM'),\n",
        "                        ('GCACGGACTGGTGGATAGTAGAGCTGTGTGA', 2, '\u003c 128 nM'),\n",
        "                        ('CACGGACTGGTGGATAGTAGAGCTGTGTGAGGTCG', 0, '\u003e 512nM'),\n",
        "                        ('CGCACGGACTGGTGGATAGTAGAGCTGTGTGAGGT', 2, '\u003c 128 nM'),\n",
        "                        ('GTCGCACGGACTGGTGGATAGTAGAGCTGTGTGAGGTCG', 3, '\u003c 32 nM'),\n",
        "                        ('GATGGTGGCTGGATAGTCA', 3, '\u003c 32 nM'),\n",
        "                        ('GATGGTGGCTGGATAGTCACCTAGTGTCTGG', 3, '\u003c 32 nM')]\n",
        "validated_core_seq_df = pd.DataFrame(validated_core_seqs, columns=['core_seq', 'stringency_level', 'Kd'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gkLvqYa8v0_B"
      },
      "source": [
        "# Load in Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89,
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "ok": true,
              "status": 200,
              "status_text": ""
            }
          }
        },
        "executionInfo": {
          "elapsed": 72315,
          "status": "ok",
          "timestamp": 1603473576839,
          "user": {
            "displayName": "Ali Bashir",
            "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg_2QzdOhvL03-igIA8CW4RWncTEV4gLZ80ECLPkg=s64",
            "userId": "01976351517997791084"
          },
          "user_tz": 420
        },
        "id": "cVTVlNJwv3Jg",
        "outputId": "9d135d36-3179-4192-f9bb-3de3919f8a83"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "     \u003cinput type=\"file\" id=\"files-eaf818cd-9261-416e-8e50-78af768c1f69\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" /\u003e\n",
              "     \u003coutput id=\"result-eaf818cd-9261-416e-8e50-78af768c1f69\"\u003e\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      \u003c/output\u003e\n",
              "      \u003cscript src=\"/nbextensions/google.colab/files.js\"\u003e\u003c/script\u003e "
            ],
            "text/plain": [
              "\u003cIPython.core.display.HTML object\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Saving truncation_option_seed_scores_manuscript.csv to truncation_option_seed_scores_manuscript.csv\n",
            "User uploaded file \"truncation_option_seed_scores_manuscript.csv\" with length 16277793 bytes\n"
          ]
        }
      ],
      "source": [
        "# All ML score truncations for G12 and G13 in all screened sequence contexts.\n",
        "# Upload truncation_option_seed_scores_manuscript.csv\n",
        "from google.colab import files\n",
        "\n",
        "uploaded = files.upload()\n",
        "\n",
        "for fn in uploaded.keys():\n",
        "  print('User uploaded file \"{name}\" with length {length} bytes'.format(\n",
        "      name=fn, length=len(uploaded[fn])))\n",
        "\n",
        "with open('truncation_option_seed_scores_manuscript.csv') as f:\n",
        "  truncation_scores_df = pd.read_csv(f)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sceRGQ0Qd2LG"
      },
      "source": [
        "# Merge experimental and model score data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Mi5CPlxn0Rmo"
      },
      "outputs": [],
      "source": [
        "trunc_validated_df = truncation_scores_df.merge(validated_core_seq_df, how='inner', on='core_seq')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B5OuLFei1Ydi"
      },
      "source": [
        "# Examine distribution of scores for sequences"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J77VU2if3Qk5"
      },
      "outputs": [],
      "source": [
        "trunc_validated_df['core_seq_len'] = trunc_validated_df['core_seq'].apply(len)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FhZvSGJN1Xue"
      },
      "outputs": [],
      "source": [
        "trunc_validated_df_median = trunc_validated_df[\n",
        "                                               [u'core_seq', u'seq', u'seed_seq', \n",
        "                                                u'seed_label', u'model_score',\n",
        "                                                u'model_delta', 'core_seq_len', \n",
        "                                                'inferer_name', 'Kd',\n",
        "                                                'stringency_level']].groupby(\n",
        "                                                    [u'core_seq', u'seed_seq', 'core_seq_len',\n",
        "                                                     u'seed_label', 'Kd',\n",
        "                                                     u'inferer_name']).median().reset_index()\n",
        "\n",
        "trunc_validated_df_var = trunc_validated_df[[u'core_seq', u'seq', u'seed_seq', \n",
        "                                             u'seed_label', u'model_score',\n",
        "                                             u'model_delta', 'core_seq_len', \n",
        "                                             'inferer_name', 'Kd',\n",
        "                                             'stringency_level']].groupby(\n",
        "                                                 [u'core_seq', u'seed_seq', 'core_seq_len', \n",
        "                                                  u'seed_label', 'Kd',\n",
        "                                                  u'inferer_name']).var().reset_index()\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 638
        },
        "executionInfo": {
          "elapsed": 896,
          "status": "ok",
          "timestamp": 1603473577777,
          "user": {
            "displayName": "Ali Bashir",
            "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg_2QzdOhvL03-igIA8CW4RWncTEV4gLZ80ECLPkg=s64",
            "userId": "01976351517997791084"
          },
          "user_tz": 420
        },
        "id": "CRrYyjcFTyrA",
        "outputId": "c5ad4c88-a44d-4566-a9fe-afa41faba84c"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\u003cdiv\u003e\n",
              "\u003cstyle scoped\u003e\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\u003c/style\u003e\n",
              "\u003ctable border=\"1\" class=\"dataframe\"\u003e\n",
              "  \u003cthead\u003e\n",
              "    \u003ctr style=\"text-align: right;\"\u003e\n",
              "      \u003cth\u003e\u003c/th\u003e\n",
              "      \u003cth\u003eseed_label\u003c/th\u003e\n",
              "      \u003cth\u003ecore_seq\u003c/th\u003e\n",
              "      \u003cth\u003ecore_seq_len\u003c/th\u003e\n",
              "      \u003cth\u003eKd\u003c/th\u003e\n",
              "      \u003cth\u003einferer_name\u003c/th\u003e\n",
              "      \u003cth\u003emodel_score_median\u003c/th\u003e\n",
              "      \u003cth\u003emodel_score_var\u003c/th\u003e\n",
              "    \u003c/tr\u003e\n",
              "  \u003c/thead\u003e\n",
              "  \u003ctbody\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e32\u003c/th\u003e\n",
              "      \u003ctd\u003eG13\u003c/td\u003e\n",
              "      \u003ctd\u003eCAAGAGGATTTGGTGGATAGTAAATCTTTGCCTATCCAG\u003c/td\u003e\n",
              "      \u003ctd\u003e39\u003c/td\u003e\n",
              "      \u003ctd\u003e\u0026lt; 32 nM\u003c/td\u003e\n",
              "      \u003ctd\u003eSuperBin\u003c/td\u003e\n",
              "      \u003ctd\u003e3.826400\u003c/td\u003e\n",
              "      \u003ctd\u003e0.015550\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e29\u003c/th\u003e\n",
              "      \u003ctd\u003eG13\u003c/td\u003e\n",
              "      \u003ctd\u003eCAAGAGGATTTGGTGGATAGTAAATCTTTGCCTAT\u003c/td\u003e\n",
              "      \u003ctd\u003e35\u003c/td\u003e\n",
              "      \u003ctd\u003e\u0026lt; 32 nM\u003c/td\u003e\n",
              "      \u003ctd\u003eSuperBin\u003c/td\u003e\n",
              "      \u003ctd\u003e3.549623\u003c/td\u003e\n",
              "      \u003ctd\u003e0.257312\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e26\u003c/th\u003e\n",
              "      \u003ctd\u003eG13\u003c/td\u003e\n",
              "      \u003ctd\u003eCAAGAGGATTTGGTGGATAGTAAATCTTTGC\u003c/td\u003e\n",
              "      \u003ctd\u003e31\u003c/td\u003e\n",
              "      \u003ctd\u003e\u0026lt; 8 nM\u003c/td\u003e\n",
              "      \u003ctd\u003eSuperBin\u003c/td\u003e\n",
              "      \u003ctd\u003e3.734980\u003c/td\u003e\n",
              "      \u003ctd\u003e0.489129\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e2\u003c/th\u003e\n",
              "      \u003ctd\u003eG13\u003c/td\u003e\n",
              "      \u003ctd\u003eAAGAGGATTTGGTGGATAGTAAATCTT\u003c/td\u003e\n",
              "      \u003ctd\u003e27\u003c/td\u003e\n",
              "      \u003ctd\u003e\u0026lt; 8 nM\u003c/td\u003e\n",
              "      \u003ctd\u003eSuperBin\u003c/td\u003e\n",
              "      \u003ctd\u003e3.740236\u003c/td\u003e\n",
              "      \u003ctd\u003e0.665158\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e38\u003c/th\u003e\n",
              "      \u003ctd\u003eG13\u003c/td\u003e\n",
              "      \u003ctd\u003eGAGGATTTGGTGGATAGTAAATCTTTG\u003c/td\u003e\n",
              "      \u003ctd\u003e27\u003c/td\u003e\n",
              "      \u003ctd\u003e\u0026lt; 8 nM\u003c/td\u003e\n",
              "      \u003ctd\u003eSuperBin\u003c/td\u003e\n",
              "      \u003ctd\u003e3.815974\u003c/td\u003e\n",
              "      \u003ctd\u003e0.635511\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e44\u003c/th\u003e\n",
              "      \u003ctd\u003eG13\u003c/td\u003e\n",
              "      \u003ctd\u003eGTGGATAGTAAATCTTTGCCTATCCAG\u003c/td\u003e\n",
              "      \u003ctd\u003e27\u003c/td\u003e\n",
              "      \u003ctd\u003e\u0026gt; 512nM\u003c/td\u003e\n",
              "      \u003ctd\u003eSuperBin\u003c/td\u003e\n",
              "      \u003ctd\u003e2.230930\u003c/td\u003e\n",
              "      \u003ctd\u003e0.940225\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e23\u003c/th\u003e\n",
              "      \u003ctd\u003eG13\u003c/td\u003e\n",
              "      \u003ctd\u003eAGAGGATTTGGTGGATAGTAAAT\u003c/td\u003e\n",
              "      \u003ctd\u003e23\u003c/td\u003e\n",
              "      \u003ctd\u003e\u0026lt; 32 nM\u003c/td\u003e\n",
              "      \u003ctd\u003eSuperBin\u003c/td\u003e\n",
              "      \u003ctd\u003e3.808052\u003c/td\u003e\n",
              "      \u003ctd\u003e0.848205\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e35\u003c/th\u003e\n",
              "      \u003ctd\u003eG13\u003c/td\u003e\n",
              "      \u003ctd\u003eGAGGATTTGGTGGATAGTAAATC\u003c/td\u003e\n",
              "      \u003ctd\u003e23\u003c/td\u003e\n",
              "      \u003ctd\u003e\u0026lt; 8 nM\u003c/td\u003e\n",
              "      \u003ctd\u003eSuperBin\u003c/td\u003e\n",
              "      \u003ctd\u003e3.745302\u003c/td\u003e\n",
              "      \u003ctd\u003e0.686720\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e41\u003c/th\u003e\n",
              "      \u003ctd\u003eG13\u003c/td\u003e\n",
              "      \u003ctd\u003eGATAGTAAATCTTTGCCTATCCA\u003c/td\u003e\n",
              "      \u003ctd\u003e23\u003c/td\u003e\n",
              "      \u003ctd\u003e\u0026gt; 512nM\u003c/td\u003e\n",
              "      \u003ctd\u003eSuperBin\u003c/td\u003e\n",
              "      \u003ctd\u003e0.219223\u003c/td\u003e\n",
              "      \u003ctd\u003e0.212515\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e56\u003c/th\u003e\n",
              "      \u003ctd\u003eG13\u003c/td\u003e\n",
              "      \u003ctd\u003eTTTGGTGGATAGTAAATCTTTGC\u003c/td\u003e\n",
              "      \u003ctd\u003e23\u003c/td\u003e\n",
              "      \u003ctd\u003e\u0026gt; 512nM\u003c/td\u003e\n",
              "      \u003ctd\u003eSuperBin\u003c/td\u003e\n",
              "      \u003ctd\u003e3.337054\u003c/td\u003e\n",
              "      \u003ctd\u003e1.402974\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e20\u003c/th\u003e\n",
              "      \u003ctd\u003eG13\u003c/td\u003e\n",
              "      \u003ctd\u003eAGAGGATTTGGTGGATAGT\u003c/td\u003e\n",
              "      \u003ctd\u003e19\u003c/td\u003e\n",
              "      \u003ctd\u003e\u0026gt; 512nM\u003c/td\u003e\n",
              "      \u003ctd\u003eSuperBin\u003c/td\u003e\n",
              "      \u003ctd\u003e3.515028\u003c/td\u003e\n",
              "      \u003ctd\u003e1.038317\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e53\u003c/th\u003e\n",
              "      \u003ctd\u003eG13\u003c/td\u003e\n",
              "      \u003ctd\u003eTTTGGTGGATAGTAA\u003c/td\u003e\n",
              "      \u003ctd\u003e15\u003c/td\u003e\n",
              "      \u003ctd\u003e\u0026lt; 512 nM\u003c/td\u003e\n",
              "      \u003ctd\u003eSuperBin\u003c/td\u003e\n",
              "      \u003ctd\u003e3.214107\u003c/td\u003e\n",
              "      \u003ctd\u003e2.216152\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e17\u003c/th\u003e\n",
              "      \u003ctd\u003eG12\u003c/td\u003e\n",
              "      \u003ctd\u003eACGTTTTTGGTGGATAGCAAATGCCAGGGCCCTTTTTTG\u003c/td\u003e\n",
              "      \u003ctd\u003e39\u003c/td\u003e\n",
              "      \u003ctd\u003e\u0026lt; 32 nM\u003c/td\u003e\n",
              "      \u003ctd\u003eSuperBin\u003c/td\u003e\n",
              "      \u003ctd\u003e4.132033\u003c/td\u003e\n",
              "      \u003ctd\u003e0.008568\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e14\u003c/th\u003e\n",
              "      \u003ctd\u003eG12\u003c/td\u003e\n",
              "      \u003ctd\u003eACGTTTTTGGTGGATAGCAAATGCCAGGGCCCTTT\u003c/td\u003e\n",
              "      \u003ctd\u003e35\u003c/td\u003e\n",
              "      \u003ctd\u003e\u0026lt; 32 nM\u003c/td\u003e\n",
              "      \u003ctd\u003eSuperBin\u003c/td\u003e\n",
              "      \u003ctd\u003e3.701966\u003c/td\u003e\n",
              "      \u003ctd\u003e0.347123\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e11\u003c/th\u003e\n",
              "      \u003ctd\u003eG12\u003c/td\u003e\n",
              "      \u003ctd\u003eACGTTTTTGGTGGATAGCAAATGCCAGGGCC\u003c/td\u003e\n",
              "      \u003ctd\u003e31\u003c/td\u003e\n",
              "      \u003ctd\u003e\u0026lt; 32 nM\u003c/td\u003e\n",
              "      \u003ctd\u003eSuperBin\u003c/td\u003e\n",
              "      \u003ctd\u003e3.519245\u003c/td\u003e\n",
              "      \u003ctd\u003e0.255186\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e8\u003c/th\u003e\n",
              "      \u003ctd\u003eG12\u003c/td\u003e\n",
              "      \u003ctd\u003eACGTTTTTGGTGGATAGCAAATGCCAG\u003c/td\u003e\n",
              "      \u003ctd\u003e27\u003c/td\u003e\n",
              "      \u003ctd\u003e\u0026lt; 32 nM\u003c/td\u003e\n",
              "      \u003ctd\u003eSuperBin\u003c/td\u003e\n",
              "      \u003ctd\u003e3.670578\u003c/td\u003e\n",
              "      \u003ctd\u003e0.567424\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e5\u003c/th\u003e\n",
              "      \u003ctd\u003eG12\u003c/td\u003e\n",
              "      \u003ctd\u003eACGTTTTTGGTGGATAGCAAATG\u003c/td\u003e\n",
              "      \u003ctd\u003e23\u003c/td\u003e\n",
              "      \u003ctd\u003e\u0026lt; 32 nM\u003c/td\u003e\n",
              "      \u003ctd\u003eSuperBin\u003c/td\u003e\n",
              "      \u003ctd\u003e3.441101\u003c/td\u003e\n",
              "      \u003ctd\u003e0.671744\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e50\u003c/th\u003e\n",
              "      \u003ctd\u003eG12\u003c/td\u003e\n",
              "      \u003ctd\u003eGTTTTTGGTGGATAGCAAA\u003c/td\u003e\n",
              "      \u003ctd\u003e19\u003c/td\u003e\n",
              "      \u003ctd\u003e\u0026lt; 32 nM\u003c/td\u003e\n",
              "      \u003ctd\u003eSuperBin\u003c/td\u003e\n",
              "      \u003ctd\u003e3.125453\u003c/td\u003e\n",
              "      \u003ctd\u003e1.224282\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e47\u003c/th\u003e\n",
              "      \u003ctd\u003eG12\u003c/td\u003e\n",
              "      \u003ctd\u003eGTTTTTGGTGGATAG\u003c/td\u003e\n",
              "      \u003ctd\u003e15\u003c/td\u003e\n",
              "      \u003ctd\u003e\u0026gt; 512nM\u003c/td\u003e\n",
              "      \u003ctd\u003eSuperBin\u003c/td\u003e\n",
              "      \u003ctd\u003e2.799400\u003c/td\u003e\n",
              "      \u003ctd\u003e2.241221\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "  \u003c/tbody\u003e\n",
              "\u003c/table\u003e\n",
              "\u003c/div\u003e"
            ],
            "text/plain": [
              "   seed_label                                 core_seq  core_seq_len  \\\n",
              "32        G13  CAAGAGGATTTGGTGGATAGTAAATCTTTGCCTATCCAG            39   \n",
              "29        G13      CAAGAGGATTTGGTGGATAGTAAATCTTTGCCTAT            35   \n",
              "26        G13          CAAGAGGATTTGGTGGATAGTAAATCTTTGC            31   \n",
              "2         G13              AAGAGGATTTGGTGGATAGTAAATCTT            27   \n",
              "38        G13              GAGGATTTGGTGGATAGTAAATCTTTG            27   \n",
              "44        G13              GTGGATAGTAAATCTTTGCCTATCCAG            27   \n",
              "23        G13                  AGAGGATTTGGTGGATAGTAAAT            23   \n",
              "35        G13                  GAGGATTTGGTGGATAGTAAATC            23   \n",
              "41        G13                  GATAGTAAATCTTTGCCTATCCA            23   \n",
              "56        G13                  TTTGGTGGATAGTAAATCTTTGC            23   \n",
              "20        G13                      AGAGGATTTGGTGGATAGT            19   \n",
              "53        G13                          TTTGGTGGATAGTAA            15   \n",
              "17        G12  ACGTTTTTGGTGGATAGCAAATGCCAGGGCCCTTTTTTG            39   \n",
              "14        G12      ACGTTTTTGGTGGATAGCAAATGCCAGGGCCCTTT            35   \n",
              "11        G12          ACGTTTTTGGTGGATAGCAAATGCCAGGGCC            31   \n",
              "8         G12              ACGTTTTTGGTGGATAGCAAATGCCAG            27   \n",
              "5         G12                  ACGTTTTTGGTGGATAGCAAATG            23   \n",
              "50        G12                      GTTTTTGGTGGATAGCAAA            19   \n",
              "47        G12                          GTTTTTGGTGGATAG            15   \n",
              "\n",
              "          Kd inferer_name  model_score_median  model_score_var  \n",
              "32   \u003c 32 nM     SuperBin            3.826400         0.015550  \n",
              "29   \u003c 32 nM     SuperBin            3.549623         0.257312  \n",
              "26    \u003c 8 nM     SuperBin            3.734980         0.489129  \n",
              "2     \u003c 8 nM     SuperBin            3.740236         0.665158  \n",
              "38    \u003c 8 nM     SuperBin            3.815974         0.635511  \n",
              "44   \u003e 512nM     SuperBin            2.230930         0.940225  \n",
              "23   \u003c 32 nM     SuperBin            3.808052         0.848205  \n",
              "35    \u003c 8 nM     SuperBin            3.745302         0.686720  \n",
              "41   \u003e 512nM     SuperBin            0.219223         0.212515  \n",
              "56   \u003e 512nM     SuperBin            3.337054         1.402974  \n",
              "20   \u003e 512nM     SuperBin            3.515028         1.038317  \n",
              "53  \u003c 512 nM     SuperBin            3.214107         2.216152  \n",
              "17   \u003c 32 nM     SuperBin            4.132033         0.008568  \n",
              "14   \u003c 32 nM     SuperBin            3.701966         0.347123  \n",
              "11   \u003c 32 nM     SuperBin            3.519245         0.255186  \n",
              "8    \u003c 32 nM     SuperBin            3.670578         0.567424  \n",
              "5    \u003c 32 nM     SuperBin            3.441101         0.671744  \n",
              "50   \u003c 32 nM     SuperBin            3.125453         1.224282  \n",
              "47   \u003e 512nM     SuperBin            2.799400         2.241221  "
            ]
          },
          "execution_count": 7,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Join Median and Var into one table to Summarize\n",
        "trunc_validated_df_median_var = trunc_validated_df_median.merge(\n",
        "    trunc_validated_df_var, \n",
        "    on=['core_seq', 'inferer_name', 'seed_label', 'seed_seq', 'core_seq_len', 'Kd'], \n",
        "    suffixes=('_median', '_var'))\n",
        "\n",
        "trunc_validated_df_median_var[trunc_validated_df_median_var.inferer_name == 'SuperBin'].sort_values(\n",
        "    by=['seed_label', 'core_seq_len'],\n",
        "    ascending=False)[['seed_label', 'core_seq', 'core_seq_len', 'Kd', 'inferer_name', \n",
        "                      'model_score_median', 'model_score_var']]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nnHXv6sMe73o"
      },
      "source": [
        "# Generate Figure Plots"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nQItPHuZ3guI"
      },
      "outputs": [],
      "source": [
        "def plot_swarm_and_box_plots (median_df, full_df, inferer_name, seed_label):\n",
        "  \"\"\"Plots swarm and boxplots for truncated sequences.\n",
        "\n",
        "  Args:\n",
        "    median_df: (pd.DataFrame) Median model scores for truncated sequence.\n",
        "    full_df: (pd.DataFrame) All model scores evaluated for each truncation.\n",
        "    inferer_name: (str) Name of model to plot data from (e.g. SuperBin)\n",
        "    seed_label: (str) Seed sequence for which to plot truncations.\n",
        "  \"\"\"\n",
        "  # Subset out seed and model for inference\n",
        "  seed_median_df = median_df[(median_df.inferer_name == inferer_name) \u0026 \n",
        "                             (median_df.seed_label == seed_label)].copy()\n",
        "  seed_full_df = full_df[(full_df.inferer_name ==  inferer_name) \u0026\n",
        "                         (full_df.seed_label == seed_label)].copy()\n",
        "\n",
        "  # Use the median df to sort the data by relative model scores\n",
        "  seed_median_df = seed_median_df.sort_values('model_delta', ascending=False)\n",
        "\n",
        "  # Create an offset to enable spacing between values of the same core seq len\n",
        "  core_seq_offset_dict = {} \n",
        "  for core_seq_len in seed_median_df.core_seq_len.unique():\n",
        "    for i, core_seq in enumerate(seed_median_df[seed_median_df.core_seq_len == core_seq_len].core_seq):\n",
        "      core_seq_offset_dict[core_seq] = i\n",
        "\n",
        "  # Apply these offsets back to the full set of evaluated points as well as medians\n",
        "  seed_full_df['seq_len_offset'] = seed_full_df['core_seq'].apply(\n",
        "      lambda x:  core_seq_offset_dict[x])\n",
        "  seed_full_df['seq_len_mod'] = seed_full_df['core_seq_len'] + seed_full_df['core_seq'].apply(\n",
        "      lambda x:  float(5 * core_seq_offset_dict[x]) / 10.)\n",
        "  seed_full_df['seq_len_mod2'] = -1 * seed_full_df['seq_len_mod']\n",
        "\n",
        "  # Create a categorical to enable ordering of colors\n",
        "  seed_full_df['$K_D$'] = pd.Categorical(seed_full_df['Kd'],\n",
        "                                         categories=['\u003e 512nM', '\u003c 512 nM', '\u003c 128 nM', '\u003c 32 nM', '\u003c 8 nM'],\n",
        "                                         ordered=True)\n",
        "\n",
        "  # Create an ordering to enable spacing of points.\n",
        "  boxplot_order = [-40. , -39.5, -39. , \n",
        "       -35.5, -35. ,  -31.5,\n",
        "       -31. , 30.5 , -28, -27.5, -27., \n",
        "       -25. , -24.5, -24. , -23.5, -23. , \n",
        "        -19.5, -19. , 15.5,\n",
        "        -15., -14.5, ]\n",
        "\n",
        "  # Only show the sizes we actually evaluated core sequences.\n",
        "  boxplot_order_strs = [40 , '', 39 , \n",
        "       '', 35 , '',  \n",
        "       31 , '', '', '', 27, \n",
        "      '', '', '', '', 23 , \n",
        "       '', 19 , \n",
        "      '', 15, '']\n",
        "\n",
        "\n",
        "  # First render points via swarm\n",
        "  boxplot_order_strs = map(str, boxplot_order_strs)\n",
        "  plt.figure(figsize=(10, 5))\n",
        "  ax = sns.swarmplot(data=seed_full_df, x='seq_len_mod2', y='model_score', \n",
        "                edgecolor='k',\n",
        "            palette='Greens', hue='$K_D$', order=boxplot_order, \n",
        "          dodge=False, size=3, zorder=0, linewidth=.2)\n",
        "\n",
        "  for artist in zip(ax.artists):\n",
        "      artist.set_edgecolor('k')\n",
        "\n",
        "  # Render boxplot on top\n",
        "  ax =sns.boxplot(data=seed_full_df, x='seq_len_mod2', y='model_score', \n",
        "              color='white', hue='$K_D$', order=boxplot_order, \n",
        "              dodge=False, linewidth=3, boxprops={'facecolor':'None'},\n",
        "              showcaps=False, showfliers=False )    \n",
        "    \n",
        "  # Formatting of Figure\n",
        "  for l in ax.lines:\n",
        "    # set median line style\n",
        "    l.set_linestyle('-')\n",
        "    l.set_color('k')\n",
        "    l.set_linewidth(2)\n",
        "    l.set_solid_capstyle('butt')\n",
        "    l.set_alpha(0.5)\n",
        "\n",
        "  xloc, xlab = plt.xticks()\n",
        "  xloc_filt = []\n",
        "  boxplot_order_strs_filt = []\n",
        "  for i in range(len(boxplot_order_strs)):\n",
        "    if boxplot_order_strs[i] != '':\n",
        "      boxplot_order_strs_filt.append(boxplot_order_strs[i])\n",
        "      xloc_filt.append(xloc[i])\n",
        "  plt.xticks(xloc_filt, boxplot_order_strs_filt)\n",
        "  plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
        "  plt.show() \n",
        "  \n",
        "  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "executionInfo": {
          "elapsed": 1187,
          "status": "ok",
          "timestamp": 1603473599304,
          "user": {
            "displayName": "Ali Bashir",
            "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg_2QzdOhvL03-igIA8CW4RWncTEV4gLZ80ECLPkg=s64",
            "userId": "01976351517997791084"
          },
          "user_tz": 420
        },
        "id": "M17liMlh3oCE",
        "outputId": "b041b5d4-4259-41ce-f5a1-149b9bb1dbb5"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "\u003cFigure size 720x360 with 1 Axes\u003e"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Figure 4A\n",
        "plot_swarm_and_box_plots(trunc_validated_df_median, trunc_validated_df,\n",
        "                         'SuperBin', 'G12')\n",
        "\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "executionInfo": {
          "elapsed": 1105,
          "status": "ok",
          "timestamp": 1603473599975,
          "user": {
            "displayName": "Ali Bashir",
            "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg_2QzdOhvL03-igIA8CW4RWncTEV4gLZ80ECLPkg=s64",
            "userId": "01976351517997791084"
          },
          "user_tz": 420
        },
        "id": "8u3OssxKfduj",
        "outputId": "f1a7c60f-fe48-4bd7-d8ed-d2b224e1f1ba"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "\u003cFigure size 720x360 with 1 Axes\u003e"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Figure 4A\n",
        "plot_swarm_and_box_plots(trunc_validated_df_median, trunc_validated_df,\n",
        "                         'SuperBin', 'G13')\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "last_runtime": {
        "build_target": "//research/biology/collaborations/aptamers:notebook",
        "kind": "shared"
      },
      "name": "Figure 4 Machine-learning-guided aptamer discovery (submission)",
      "provenance": [
        {
          "file_id": "1xWFpHj7qz5kDHfIOYS7Q3ErHoxOrrArR",
          "timestamp": 1603475287380
        },
        {
          "file_id": "1qC0IU5rTethsx5hb-KqMw7uLHh8eahQq",
          "timestamp": 1603357299400
        },
        {
          "file_id": "161DERdnpFdJ3b7U3YOCo7C94D39oli_U",
          "timestamp": 1603355850507
        },
        {
          "file_id": "1yp54vNxJnzTZRSG0VWNiy036HQqITtNb",
          "timestamp": 1603342615794
        },
        {
          "file_id": "1aMlzPCcPcP93hbyywBzxRW05IToy5WxQ",
          "timestamp": 1603318107917
        },
        {
          "file_id": "1eDIAaYaT_2DVqv1uR_H504yCYaas939c",
          "timestamp": 1597677819315
        },
        {
          "file_id": "1uxfPeOwc8OPbRZ9TBMlqHyyzC3tw8avq",
          "timestamp": 1588018488807
        },
        {
          "file_id": "1AAN4PglVyFue88XBcP3rSZ48UK8wz98W",
          "timestamp": 1580612333617
        },
        {
          "file_id": "1D2MWuAOiPdkU_H0k9kIpEg-cDIex1mDd",
          "timestamp": 1570722471047
        },
        {
          "file_id": "1F2MKN4sZfELaDqrVQDHu2t6lYuvBfv7V",
          "timestamp": 1551986263518
        },
        {
          "file_id": "1FMtY5x-zZ5h0Xyhm5vsoxO0WBFfROa3X",
          "timestamp": 1551304638113
        }
      ]
    },
    "kernelspec": {
      "display_name": "Python 2",
      "name": "python2"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
